code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
# Get all tracts within certain cities
Given a CSV file containing city names, get all the tracts within those cities' boundaries.
```
import geopandas as gpd
import json
import os
import pandas as pd
all_tracts_path = 'data/us_census_tracts_2014'
places_path = 'data/us_census_places_2014'
states_by_fips_path = 'dat... | github_jupyter |
By the end of this activity, you will be able to perform the following in Spark:
Determine the accuracy of a classifier model
Display the confusion matrix for a classifier model
In this activity, you will be programming in a Jupyter Python Notebook. If you have not already started the Jupyter Notebook server, see the ... | github_jupyter |
## Computing native contacts with MDTraj
Using the definition from Best, Hummer, and Eaton, "Native contacts determine protein folding mechanisms in atomistic simulations" PNAS (2013) [10.1073/pnas.1311599110](http://dx.doi.org/10.1073/pnas.1311599110)
Eq. (1) of the SI defines the expression for the fraction of nati... | github_jupyter |
First, load the data, from the supplied data file
```
import tarfile
import json
import gzip
import pandas as pd
import botometer
from pandas.io.json import json_normalize
## VARIABLE INITIATION
tar = tarfile.open("../input/2017-09-22.tar.gz", "r:gz")
mashape_key = "QRraJnMT9KmshkpJ7iu74xKFN1jtp1IyBBijsnS5NGbEuwIX54"... | github_jupyter |
Wayne H Nixalo - 09 Aug 2017
FADL2 L9: Generative Models
neural-style-GPU.ipynb
```
%matplotlib inline
import importlib
import os, sys
sys.path.insert(1, os.path.join('../utils'))
from utils2 import *
from scipy.optimize import fmin_l_bfgs_b
from scipy.misc import imsave
from keras import metrics
from vgg16_avg im... | github_jupyter |
```
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.ticker as mtick
from dateutil.parser import parse as date_parse
import requests
%matplotlib inline
pd.options.mode.chained_assignment = None
jhu_data = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/' \
... | github_jupyter |
# The Central Limit Theorem
Elements of Data Science
by [Allen Downey](https://allendowney.com)
[MIT License](https://opensource.org/licenses/MIT)
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
... | github_jupyter |
```
%tensorflow_version 2.x
%load_ext tensorboard
import tensorflow as tf
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt
from os import path, walk
import numpy as np
import datetime
from skimage import feature, util, io, color
import cv2
device_name = tf.test.gpu_device_name()
if device_... | github_jupyter |
### **PINN eikonal solver using transfer learning for a smooth v(x,z) model**
```
from google.colab import drive
drive.mount('/content/gdrive')
cd "/content/gdrive/My Drive/Colab Notebooks/Codes/PINN_isotropic_eikonal"
!pip install sciann==0.4.6.2
!pip install tensorflow==2.2.0
!pip install keras==2.3.1
import numpy a... | github_jupyter |
# Qcodes example with Alazar ATS 9360
```
# import all necessary things
%matplotlib nbagg
import qcodes as qc
import qcodes.instrument.parameter as parameter
import qcodes.instrument_drivers.AlazarTech.ATS9360 as ATSdriver
import qcodes.instrument_drivers.AlazarTech.ATS_acquisition_controllers as ats_contr
# Command ... | github_jupyter |
```
source("base/it-402-dc-common_vars.r")
# library(tidyverse) - called in common_vars
library(assertr)
```
## Notes
#### Legal (ISO) gender types:
* https://data.gov.uk/education-standards/sites/default/files/CL-Legal-Sex-Type-v2-0.pdf
#### For data from 2010 and all stored as %
* need to relax sum to 100%
* ... | github_jupyter |
# Machine Translation and the Dataset
:label:`sec_machine_translation`
We have used RNNs to design language models,
which are key to natural language processing.
Another flagship benchmark is *machine translation*,
a central problem domain for *sequence transduction* models
that transform input sequences into output s... | github_jupyter |
# Using BagIt to tag oceanographic data
[`BagIt`](https://en.wikipedia.org/wiki/BagIt) is a packaging format that supports storage of arbitrary digital content. The "bag" consists of arbitrary content and "tags," the metadata files. `BagIt` packages can be used to facilitate data sharing with federal archive centers ... | github_jupyter |
Exercise 1 (5 points): Discrete Naive Bayes Classifier [Pen and Paper]
In this exercise, we want to get a basic idea of the naive Bayes classifier by analysing a small
example. Suppose we want to classify fruits based on the criteria length, sweetness and the colour
of the fruit and we already spent days by categorizin... | github_jupyter |
# Assignment 2: Parts-of-Speech Tagging (POS)
Welcome to the second assignment of Course 2 in the Natural Language Processing specialization. This assignment will develop skills in part-of-speech (POS) tagging, the process of assigning a part-of-speech tag (Noun, Verb, Adjective...) to each word in an input text. Tag... | github_jupyter |
# <center>RumbleDB sandbox</center>
This is a RumbleDB sandbox that allows you to play with simple JSONiq queries.
It is a jupyter notebook that you can also download and execute on your own machine, but if you arrived here from the RumbleDB website, it is likely to be shown within Google's Colab environment.
To get... | github_jupyter |
# Aggregating statistics
```
import pandas as pd
air_quality = pd.read_pickle('air_quality.pkl')
air_quality.info()
```
### Series/one column of a DataFrame
```
air_quality['TEMP'].count()
air_quality['TEMP'].mean()
air_quality['TEMP'].std()
air_quality['TEMP'].min()
air_quality['TEMP'].max()
air_quality['TEMP'].qua... | github_jupyter |
# Getting Started with CREST
CREST is a hybrid modelling DSL (domain-specific language) that focuses on the flow of resources within cyber-physical systems (CPS).
CREST is implemented in the Python programming language as the `crestdsl` internal DSL and shipped as Python package.
`crestdsl`'s source code is hosted o... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | github_jupyter |
# Tests on PDA
```
import sys
sys.path[0:0] = ['../..', '../../3rdparty'] # Append to the beginning of the search path
from jove.SystemImports import *
from jove.DotBashers import *
from jove.Def_md2mc import *
from jove.Def_PDA import *
```
__IMPORTANT: Must time-bound explore-pda, run-pda, explore-tm,... | github_jupyter |
# esBERTus: evaluation of the models results
In this notebook, an evaluation of the results obtained by the two models will be performed. The idea here is not as much to measure a benchmarking metric on the models but to understand the qualitative difference of the models.
In order to do so
## Keyword extraction
In o... | github_jupyter |
# 对象和类
- 一个学生，一张桌子，一个圆都是对象
- 对象是类的一个实例，你可以创建多个对象，创建类的一个实例过程被称为实例化，
- 在Python中对象就是实例，而实例就是对象
## 定义类
class ClassName:
do something
- class 类的表示与def 一样
- 类名最好使用驼峰式
- 在Python2中类是需要继承基类object的，在Python中默认继承，可写可不写
- 可以将普通代码理解为皮肤，而函数可以理解为内衣，那么类可以理解为外套
```
# 类必须初始化,是用self,初始化自身.
# 类里面所有的函数中的第一个变量不再是参数,而是一个印记.
# 在类中,... | github_jupyter |
# Transfer Learning Template
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os, json, sys, time, random
import numpy as np
import torch
from torch.optim import Adam
from easydict import EasyDict
import matplotlib.pyplot as plt
from steves_models.steves_ptn import Steves_Prototypical_Network
... | github_jupyter |
## Week 4
## T - testing and Inferential Statistics
Most people turn to IMB SPSS for T-testings, but this programme is very expensive, very old and not really necessary if you have access to Python tools. Very focused on click and point and is probably more useful to people without a programming background.
### Lib... | github_jupyter |
```
# Загрузка зависимостей
import numpy
import pandas
import matplotlib.pyplot
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
# Загрузка и анализ набора данных
raw_dataset = pandas.read_csv('machine.data.csv', header=None) # Убедиться в правильности пути к файлу!
raw_da... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Explore Duplicate Question Matches
Use this dashboard to explore the relationship between duplicate and original questions.
## Setup
This section loads needed packages, and defines useful functions.
```
from __future__ impo... | github_jupyter |
<h1 align="center"> Registration Initialization: We Have to Start Somewhere</h1>
Initialization is a critical aspect of most registration algorithms, given that most algorithms are formulated as an iterative optimization problem.
In many cases we perform initialization in an automatic manner by making assumptions wit... | github_jupyter |
<a href="https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Finetune 🤗 Transformers Models with PyTorch Lightning ⚡... | github_jupyter |
# Spreadsheets Functions
## Logical
#### `=IF(logical_test, value_if_true, value_if_false)`
#### `Comparison operators: =, >, <, >=, <=, <>`
### `Comparison Functions : `
#### `=ISNA()`
#### `=ISNUMBER()`
#### `=ISTEXT()`
#### `=ISBLANK()`
#### `=ISNONTEXT()`
#### `=ISLOGICAL()`
## Text
#### `FIND("!", mytext):` Fi... | github_jupyter |
# Convolutional Neural Networks: Application
Welcome to Course 4's second assignment! In this notebook, you will:
- Implement helper functions that you will use when implementing a TensorFlow model
- Implement a fully functioning ConvNet using TensorFlow
**After this assignment you will be able to:**
- Build and t... | github_jupyter |
# Rebound model
Aim: Quantify the environmental impact due to the savings of households in consumption expenses, across different
- industrial sectors and scenarios:
- housing (rent): baseline for 2011,
- energy: efficient_devices, renewable_energy
- food-waste: avoidable_waste_saving
- clothing: su... | github_jupyter |
# **Regressão Linear**
#### Este notebook mostra uma implementação básica de Regressão Linear e o uso da biblioteca [MLlib](http://spark.apache.org/docs/1.4.0/api/python/pyspark.ml.html) do PySpark para a tarefa de regressão na base de dados [Million Song Dataset]... | github_jupyter |
# Anomaly detection
Anomaly detection is a machine learning task that consists in spotting so-called outliers.
“An outlier is an observation in a data set which appears to be inconsistent with the remainder of that set of data.”
Johnson 1992
“An outlier is an observation which deviates so much from the other observa... | github_jupyter |
# Intro to machine learning - k-means
---
Scikit-learn has a nice set of unsupervised learning routines which can be used to explore clustering in the parameter space.
In this notebook we will use k-means, included in Scikit-learn, to demonstrate how the different rocks occupy different regions in the available param... | github_jupyter |
# Beating the betting firms with linear models
* **Data Source:** [https://www.kaggle.com/hugomathien/soccer](https://www.kaggle.com/hugomathien/soccer)
* **Author:** Anders Munk-Nielsen
**Result:** It is possible to do better than the professional betting firms in terms of predicting each outcome (although they may ... | github_jupyter |
# Assignment 2: Naive Bayes
Welcome to week two of this specialization. You will learn about Naive Bayes. Concretely, you will be using Naive Bayes for sentiment analysis on tweets. Given a tweet, you will decide if it has a positive sentiment or a negative one. Specifically you will:
* Train a naive bayes model on a... | github_jupyter |
```
from graph2text.finetune import SummarizationModule, Graph2TextModule
import argparse
import pytorch_lightning as pl
import os
import sys
from pathlib import Path
import pdb
SEED = 42
import torch
torch.cuda.is_available(), torch.cuda.device_count()
MODEL='t5-base'
DATA_DIR = './graph2text/data/webnlg'
OUTPUT_DIR ... | github_jupyter |
```
%matplotlib inline
import numpy as np
import seaborn
import nltk
from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics import classification_report
# prepare corpus
corpus = []
for d in range(1400... | github_jupyter |
```
import keras
keras.__version__
```
# Using a pre-trained convnet
This notebook contains the code sample found in Chapter 5, Section 3 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in partic... | github_jupyter |
# Filled Julia set
___
Let $C\in \mathbb{C}$ is fixed. A *Filled Julia set* $K_C$ is the set of $z\in \mathbb{C}$ which satisfy $\ f^n_C(z)$ $(n \ge 1)$is bounded :
$$K_C = \bigl\{ z\in \mathbb{C}\bigm|\{f^n_C(z)\}_{n\ge 1} : bounded\bigr\},$$
where $\ \ f^1_C(z) = f_C(z) = z^2 + C $, $\ \ f^n_C = f^{n-1}_C \circ f_C$... | github_jupyter |
#if package is not installed and path not set correct - this helps you out :)
sys.path.append(root_dir+"/..")
import pygromos
from pygromos.gromos.gromosPP import GromosPP
from pygromos.gromos.gromosXX import Gromos... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Project: **Finding Lane Lines on the Road**
***
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really j... | github_jupyter |
```
import os
from glob import glob
import random
import torch
from torchvision import datasets as dset
from torchvision import transforms
from matplotlib import pyplot as plt
from torch.utils.data import DataLoader, Dataset
from tqdm.notebook import tqdm
from siamesenet import SiameseNet
from arguments import get_co... | github_jupyter |
```
import numpy as np
import heron
import heron.models.georgebased
generator = heron.models.georgebased.Heron2dHodlrIMR()
generator.parameters = ["mass ratio"]
times = np.linspace(-0.05, 0.05, 1000)
hp, hx = generator.mean({"mass ratio": 1}, times)
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(hp.data)
s... | github_jupyter |
<img src="https://upload.wikimedia.org/wikipedia/commons/4/47/Logo_UTFSM.png" width="200" alt="utfsm-logo" align="left"/>
# MAT281
### Aplicaciones de la Matemática en la Ingeniería
## Módulo 03
## Clase 01: Teoría y Landscape de Visualizaciones
## Objetivos
* Comprender la importancia de las visualizaciones.
* Con... | github_jupyter |
<a href="https://colab.research.google.com/github/Rivaldop/metodologidatascience/blob/main/Regresi_Linear.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<img src = "https://evangsmailoa.files.wordpress.com/2019/09/ml.png" align = "center">
#<cent... | github_jupyter |
# Tutorial Part 10: Exploring Quantum Chemistry with GDB1k
Most of the tutorials we've walked you through so far have focused on applications to the drug discovery realm, but DeepChem's tool suite works for molecular design problems generally. In this tutorial, we're going to walk through an example of how to train a ... | github_jupyter |
<a href="https://colab.research.google.com/github/Chiebukar/Deep-Learning/blob/main/regression/temperature_forcasting_with_RNN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Temperature Forcasting with Jena climate dataset
```
from google.colab... | github_jupyter |
### PPO, Actor-Critic Style
_______________________
**for** iteration=1,2,... do<br>
**for** actor=1,2,...,N do<br>
Run policy $\pi_{\theta_{old}}$ in environment for T timesteps<br>
 ... | github_jupyter |
<h1> Create TensorFlow model </h1>
This notebook illustrates:
<ol>
<li> Creating a model using the high-level Estimator API
</ol>
```
# change these to try this notebook out
BUCKET = 'qwiklabs-gcp-37b9fafbd24bf385'
PROJECT = 'qwiklabs-gcp-37b9fafbd24bf385'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCK... | github_jupyter |
<img style="float: right; margin: 0px 0px 15px 15px;" src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQt6eQo8JPYzYO4p6WmxLtccdtJ4X8WR6GzVVKbsMjyGvUDEn1mg" width="300px" height="100px" />
# Trabajando con opciones
Una opción puede negociarse en el mercado secundario por lo que es importante determinar su v... | github_jupyter |
# List and Dictionary Comprehensions
Comprehension is a different way to construct lists and dictionaries. Up to now, every time that we have built up a list or dictionary, we began by initializing it. We then took advantage of their mutability inherent to build them up one element or key-value pair at a time. However... | github_jupyter |
```
import numpy as np
import pandas as pd
from pathlib import Path
from matplotlib import pyplot as plt
from sklearn.preprocessing import LabelEncoder
train_df = pd.read_csv(Path('Resources/2019loans.csv'))
test_df = pd.read_csv(Path('Resources/2020Q1loans.csv'))
train_df
test_df
# Convert categorical data to numeric ... | github_jupyter |
## Denoising Autoencoder on MNIST dataset
* This notebook will give you a very good understanding abou denoising autoencoders
* For more information: visit [here](https://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html)
* The entire notebook is in PyTorch
```
# Importing packages that will be... | github_jupyter |
# Cleaning the data to build the prototype for crwa
### This data cleans the original sql output and performs cleaning tasks. Also checking validity of the results against original report found at
### https://www.crwa.org/uploads/1/2/6/7/126781580/crwa_ecoli_web_2017_updated.xlsx
```
import pandas as pd
pd.options.di... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.formula.api as sm
%matplotlib inline
diab = pd.read_csv("../data/diabetes.csv")
print("""
# Variables are
# subject: subject ID number
# age: age diagnosed with diabetes
# acidity: a measure of acidity called base deficit
# ... | github_jupyter |
# MATH 4100: Temporal data analysis and applications to stock analysis
*Curtis Miller*
## Introduction
This is a lecture for [MATH 4100/CS 5160: Introduction to Data Science](http://datasciencecourse.net/), offered at the University of Utah, introducing time series data analysis applied to finance.
Advanced mathemat... | github_jupyter |
# Вебинар 6. Консультация по курсовому проекту.
### Задание для курсового проекта
Метрика:
R2 - коэффициент детерминации (sklearn.metrics.r2_score)
Сдача проекта:
1. Прислать в раздел Задания Урока 10 ("Вебинар. Консультация по итоговому проекту")
ссылку на программу в github (программа должна содержаться в файле Ju... | github_jupyter |
```
#'''
#Demonstrates GRAPPA reconstruction of undersampled data.
#See function grappa_detail.py for an example showing more of the
#workings and functionality of the SIRF code.
#
#Pre-requisites:
# 1) If the reconstruction engine is set to Gadgetron (default), then
# this Python script needs to be able to access ... | github_jupyter |
# Radial Velocity Orbit-fitting with RadVel
## Week 6, Intro-to-Astro 2021
### Written by Ruben Santana & Sarah Blunt, 2018
#### Updated by Joey Murphy, June 2020
#### Updated by Corey Beard, July 2021
## Background information
Radial velocity measurements tell us how the velocity of a star changes along the directi... | github_jupyter |
```
import math
import string
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import logit
from IPython.display import display
import tensorflow as tf
from tensorflow.keras.layers import (Input, Dense, Lambda, Flatten, Reshape, BatchNormalization, Layer,
... | github_jupyter |
# Calculating a custom statistic
This example shows how to define and use a custom `iris.analysis.Aggregator`, that provides a new statistical operator for
use with cube aggregation functions such as `~iris.cube.Cube.collapsed`, `~iris.cube.Cube.aggregated_by` or `~iris.cube.Cube.rolling_window`.
In this case, we hav... | github_jupyter |
```
import pandas as pd
import geopandas
import glob
import matplotlib.pyplot as plt
import numpy as np
import seaborn
import shapefile as shp
from paths import *
from refuelplot import *
setup()
wpNZ = pd.read_csv(data_path + "/NZ/windparks_NZ.csv", delimiter=';')
wpBRA = pd.read_csv(data_path + '/BRA/turbine_data.csv... | github_jupyter |
# Test Coffea
This will test Coffea to see if we can figure out how to use it with our code.
First are the includes from coffea. This is based on the [example written by Ben](https://github.com/CoffeaTeam/coffea/blob/master/binder/servicex/ATLAS/LocalExample.ipynb).
```
from servicex import ServiceXDataset
from cof... | github_jupyter |
# dwtls: Discrete Wavelet Transform LayerS
This library provides downsampling (DS) layers using discrete wavelet transforms (DWTs), which we call DWT layers.
Conventional DS layers lack either antialiasing filters and the perfect reconstruction property, so downsampled features are aliased and entire information of inp... | github_jupyter |
```
# Imports
import matplotlib.pyplot as plt
import json
# Load data from result files
results_file = './results/results_5.json'
summary_file = './results/summary.json'
results = json.load(open(results_file))['results']
summary = json.load(open(summary_file))
def autolabel(rects, label_pos=0):
"""
Generate ... | github_jupyter |
<a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/sprinkler_pgm.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Directed graphical models
We illustrate some basic properties of DGMs.
```
!pip install causal... | github_jupyter |
```
import json
from datetime import datetime, timedelta
import matplotlib.pylab as plot
import matplotlib.pyplot as plt
from matplotlib import dates
import pandas as pd
import numpy as np
import matplotlib
matplotlib.style.use('ggplot')
%matplotlib inline
# Read data from http bro logs
with open("http.log",'r') as in... | github_jupyter |
# Method4 DCT based DOST + Huffman encoding
## Import Libraries
```
import mne
import numpy as np
from scipy.fft import fft,fftshift
import matplotlib.pyplot as plt
from scipy.signal import butter, lfilter
from scipy.signal import freqz
from scipy import signal
from scipy.fftpack import fft, dct, idct
from itertools ... | github_jupyter |
# ElasticNet with RobustScaler
**This Code template is for the regression analysis using a ElasticNet Regression and the feature rescaling technique RobustScaler in a pipeline**
### Required Packages
```
import warnings as wr
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot... | github_jupyter |
# Let's Import Our Libraries
```
# Keras
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation
from keras.layers.embeddings import Embedding
# Pl... | github_jupyter |
<a href="https://colab.research.google.com/github/sroy8091/deep-learning-v2-pytorch/blob/master/convolutional-neural-networks/cifar-cnn/cifar10_cnn_exercise.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Convolutional Neural Networks
---
In this ... | github_jupyter |
# Quickstart
In this tutorial, we explain how to quickly use ``LEGWORK`` to calculate the detectability of a collection of sources.
```
%matplotlib inline
```
Let's start by importing the source and visualisation modules of `LEGWORK` and some other common packages.
```
import legwork.source as source
import legwork.... | github_jupyter |
- **Let us see how well our model would perform if we would deploy our model at the end of 2018**
- **ie: Let us test our model on 2019 data**
```
import numpy as np
import pandas as pd
import category_encoders as ce
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OneHotEncoder
dat... | github_jupyter |
```
import transportation_tutorials as tt
```
# Creating Dynamic Maps
In this gallery, we will demonstrate the creation of a variety of interactive maps.
Interactive, dynamic maps are a good choice for analytical work that will be reviewed
online, either in a Jupyter notebook by an analyst, or published on a website.... | github_jupyter |
### Data Scientist Nano Degree - Capstone Project
### Car Booking Analysis and Prediction
### Tarek Abd ElRahman Ahmed ElAyat
#### Let's import the needed libraries
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import warnings
from sklearn import model_selection
fr... | github_jupyter |
```
from __future__ import division, print_function
import os
import torch
import pandas
import numpy as np
from torch.utils.data import DataLoader,Dataset
from torchvision import utils, transforms
from skimage import io, transform
import matplotlib.pyplot as plt
import warnings
#ignore warnings
warnings.filterwarning... | github_jupyter |
## K Means Clustering
### Our Objective - Perform K-Means Clustering to detect Network Intrusion Attempts (Cybersecurity)
```
#matrix math
import numpy as np
#graphing
import matplotlib.pyplot as plt
#graphing animation
import matplotlib.animation as animation
#load textfile dataset (2D data points)
# for each user, h... | github_jupyter |
### B.1.1.7
#### The selection of suitable loci (done on Tue 22. Dec. 2020)
To identify suitable targets for primer/probe design, we downloaded 1,136 sequences from the GISAID repository filtered during a collection time spanning 1 - 21 December 2020. We focused on the spike gene because lineage B.1.1.7 contains a num... | github_jupyter |
```
%matplotlib inline
import gym
import matplotlib
import numpy as np
import sys
from collections import defaultdict
if "../" not in sys.path:
sys.path.append("../")
from lib.envs.blackjack import BlackjackEnv
from lib import plotting
matplotlib.style.use('ggplot')
env = BlackjackEnv()
def mc_prediction(policy,... | github_jupyter |
# Generate correction profiles for denoised
by Pu Zheng
2019.06.18
```
%run "E:\Users\puzheng\Documents\Startup_py3.py"
sys.path.append(r"E:\Users\puzheng\Documents")
import ImageAnalysis3 as ia
%matplotlib notebook
from ImageAnalysis3 import *
print(os.getpid())
reload(ia.get_img_info)
reload(ia.corrections)
rel... | github_jupyter |
<a href="https://colab.research.google.com/github/haribharadwaj/notebooks/blob/main/BME511/ProbabilisticClassificationClustering.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Classification and clustering: probabilistic modeling approach
Here, ... | github_jupyter |
```
#Fill the paths below
PATH_FRC = "" # git repo directory path
PATH_ZENODO = "" # Data and models are available here: https://zenodo.org/record/5831014#.YdnW_VjMLeo
DATA_FLAT = PATH_ZENODO+'/data/goi_1000/flat_1000/*.png'
DATA_NORMAL = PATH_ZENODO+'/data/goi_1000/standard_1000/*.jpg'
GAUSS_L2_MODEL = PATH_ZENODO+'... | github_jupyter |
```
import pandas
df = pandas.read_csv(
'https://archive.ics.uci.edu/ml/'
'machine-learning-databases/iris/iris.data',
header=None,
)
df.tail()
import numpy
targets = df.iloc[0:100, 4].values
targets = numpy.where(targets == 'Iris-setosa', -1, 1)
targets[:10]
samples = df.iloc[0:100, [0, 2]].values
samples.... | github_jupyter |
# Autonomous driving - Car detection
Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (https://arxiv.org/abs/1506.02640) and Redmon and Farhadi, 2016 (htt... | github_jupyter |
```
import datetime
import os, sys
import numpy as np
import matplotlib.pyplot as plt
import casadi as cas
import pickle
import copy as cp
# from ..</src> import car_plotting
# from .import src.car_plotting
PROJECT_PATH = '/home/nbuckman/Dropbox (MIT)/DRL/2020_01_cooperative_mpc/mpc-multiple-vehicles/'
sys.path.appe... | github_jupyter |
```
# %load CommonFunctions.py
# # COMMON ATOMIC AND ASTRING FUNCTIONS
# In[14]:
############### One String Pulse with width, shift and scale #############
def StringPulse(String1, t: float, a = 1., b = 0., c = 1., d = 0.) -> float:
x = (t - b)/a
if (x < -1):
res = -0.5
elif (x > 1):
res... | github_jupyter |
```
import numpy as np
from exploration.config import sql_inst, mongo_inst
val_random_db = mongo_inst['val_random_db']
val_dump = (val_random_db['osu_scores_high'], val_random_db['osu_user_stats'])
pdf_func = np.load("exploration/skill_biased_sampling_function/pdf_sample_func.npy")
greedy_func = np.load("exploration/... | github_jupyter |
```
import ast
from glob import glob
import sys
import os
from copy import deepcopy
import networkx as nx
from stdlib_list import stdlib_list
STDLIB = set(stdlib_list())
CONVERSIONS = {
'attr': 'attrs',
'PIL': 'Pillow',
'Image': 'Pillow',
'mpl_toolkits': 'matplotlib',
'dateutil': 'python-dateutil... | github_jupyter |
# Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and... | github_jupyter |
# HRNet for MARS Tutorial
This notebook will walk through using the [HRNet pose estimator](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch) with the [data](https://data.caltech.edu/records/2011) used in the [Mouse Action Recognition System](https://www.biorxiv.org/content/10.1101/2020.07.26.222299v1).
... | github_jupyter |
```
import pandas as pd
import numpy as np
%matplotlib inline
import joblib
import json
import tqdm
import glob
import numba
import dask
import xgboost
from dask.diagnostics import ProgressBar
import re
ProgressBar().register()
fold1, fold2 = joblib.load("./valid/fold1.pkl.z"), joblib.load("./valid/fold2.pkl.z")
tra... | github_jupyter |
```
import os
import time
import numpy as np
import tensorflow as tf
from tensorflow.random import set_seed
from math import factorial
from scipy.stats import norm
from scipy.integrate import odeint
import numpy.polynomial.hermite_e as H
from sklearn.preprocessing import StandardScaler
import dolfin as fn
from numpy.p... | github_jupyter |
# EDA of All Sides Media ratings for 'debiaser' data product
#### Sagar Setru, September 21th, 2020
## Brief description using CoNVO framework
### Context
Some people are eager to get news from outside of their echo chamber. However, they do not know where to go outside of their echo chambers, and may also have some... | github_jupyter |
# Task 1: Word Embeddings (10 points)
This notebook will guide you through all steps necessary to train a word2vec model (Detailed description in the PDF).
## Imports
This code block is reserved for your imports.
You are free to use the following packages:
(List of packages)
```
# Imports
from pandas import Dat... | github_jupyter |
```
# The purpose of this notebook is to compare the
# efficacy of various Machine Learning models on
# the dataset.
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
np.random.seed(0)
script_dir = os.path.abspath('')
file = os.path.realpath(script_dir + '/../data... | github_jupyter |
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